\(G_\text{Pr}\) : In implementations, we follow [1]} to construct and initialize the medical knowledge graph. Specifically, based on the training corpus, for all images, we first build a universal graph \(G_\text{Uni} = (V, E)\) , which models the domain-specific prior knowledge structure. In detail, we compose a graph that covers the most common abnormalities or findings. In particular, we use the common topics in the topic bag \(T\) . These \(N_\text{T}\)  common topics in \(T\)  are defined as nodes \(V\)  and are grouped by the organ or body part that they relate to. For topics grouped together, we connect their nodes with bidirectional edges, resulting in closely connected related topics. After that, guided by the input image \(I\) , we can acquire a set of nodes \(V^{\prime } = \lbrace v^{\prime }_1,v^{\prime }_2,\dots ,v^{\prime }_{N_\text{T}}\rbrace  \in \mathbb {R}^{N_\text{T} \times d}\)  encoded by a graph embedding module, which is based on the graph convolution operation [2]}. We regard the encoded \(V^{\prime }\)  as the prior knowledge \(G_\text{Pr} \in \mathbb {R}^{N_\text{T} \times d}\) .
Due to space limit, please refer to [1]} for the detailed description of medical knowledge graph.
